Machine Learning Methods & Theory

We actively develop new methodology and explore learning theory relevant to applications in medical imaging. Areas of research include meta-learning, multi-task & continual learning, causality, domain shift, geometric deep learning, semi-supervised and unsupervised learning, representation learning and Bayesian methods.

Representation Learning

Morpho-MNIST: Quantitative Assessment and Diagnostics for Representation Learning (JMLR 2019)

Machine Learning on Graphs

Overfitting & Class-Imbalance

Overfitting of neural nets under class imbalance: Analysis and improvements for segmentation (MICCAI 2019)

Bayesian Deep Learning

Implicit Weight Uncertainty in Neural Networks

Domain Shift

Domain Generalization via Model-Agnostic Learning of Semantic Features (NeurIPS 2019)

Unsupervised Domain Adaptation (IPMI 2017) and PnP-AdaNet (IEEE Access 2019)

Semi-Supervised Learning

Semi-supervised learning via compact latent space clustering (ICML 2018)

Multi-Task & Continual Learning

Towards continual learning in medical imaging (NeurIPS Workshop 2018).

Causality in Imaging

Details coming soon…

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